SOTAVerified

Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1581–1590 of 1718 papers

TitleStatusHype
Emergent Resource Exchange and Tolerated Theft Behavior using Multi-Agent Reinforcement Learning—0
Empathic Coupling of Homeostatic States for Intrinsic Prosociality—0
EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System—0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Enabling Multi-Robot Collaboration from Single-Human Guidance—0
Enabling the Wireless Metaverse via Semantic Multiverse Communication—0
EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management—0
Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks—0
Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning—0
Energy-Efficient Flying LoRa Gateways: A Multi-Agent Reinforcement Learning Approach—0
Show:102550
← PrevPage 159 of 172Next →
All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified